aWhere Case Study: Eastern Africa Regional Assessment

aWhere Case Study: Eastern Africa Regional Assessment


aWhere’s assessment of a  5-country region (Kenya, Uganda, Tanzania, Rwanda, Burundi) in Eastern Africa shows alarming trends for farmers for the October rains. The ‘October rains’ over East Africa are characterized as ‘variable’ both in space and over time.   During the October rains of 2019, the season was wet yet the trend in rainfall comparing the past four October rains (2016-2019) to those of 2006-2009 (map below) shows a strong drying pattern in both Eastern Kenya and central Tanzania based on the index of Precipitation over Potential Evapotranspiration (P/PET) – a key metric for water stress in crops.

P/PET is a rapid tool for assessing moisture conditions suitable for rainfed crop production. As Precipitation (P) drops below PET (the evaporative demand of the environment), the result is drying conditions leading to plants wilting. The threshold for maize is 0.8, when P/PET is less than 0.8, maize is likely to fail. Water stress becomes acute as the P/PET ratio drops below 0.7 and below 0.6 the ecology shifts to grassland and then below 0.4 is when desert conditions prevail.

The charts below show how last season for a specific location deviated from the regional drying trend and points to why local weather analytics are increasingly important to help farmers adapt to climate change.

Analytics: Understanding Weather Variability

aWhere analysts highlight coefficient of variation (CV) to describe  the extent of weather variability in specific locations. A  CV greater than 20% is considered highly variable. Nearly 79% of the population (~161M people) lives in areas where the variance in Precipitation/Potential Evapotranspiration (P/PET) for the October growing season is greater than 20%. Why is this important?  When variability is too high, farmers are at risk of not having a reliable cropping season. Variability in rainfall and P/PET poses a great risk to rainfed farmers who over time lose confidence in investing in good agronomic practices. Over time, this  leads to lower productivity and risk of food insecurity. 

The map below shows the Coefficient of Variation for P/PET from October-December 2006-2019. The additional charts reveal the precipitation trends from 2006-2019 for specific locations (latitude and longitude) for October-December.

Implications and Recommendations

Across East Africa, changes in soil moisture availability to support agriculture have shifted and variability has increased.  To adapt, farmers need in-time weather-based insights to make informed crop management decisions to increase productivity and reduce the risk of crop failures.  Fortunately we have the tools and partnerships  now to address this knowledge gap – today. Accurate weather data in the hands of a farmer enables resilience to weather variability and informs adaptation to climate change. 

Contact aWhere for more information: Analytics driving economic resilience to climate change